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This guide provides three different methods to install PyTorch with GPU acceleration using CUDA and cuDNN. Choose the method that best suits your requirements and system configuration.
- Using Virtual Environment (venv) in Python
- Using Conda Environment in Anaconda
- Using pip
- Using conda
- Using Docker Container
| passwd deck | |
| sudo steamos-readonly disable | |
| sudo systemctl enable --now sshd | |
| sudo sed -i -E 's/#?PasswordAuthentication yes/PasswordAuthentication no/' /etc/ssh/sshd_config | |
| sudo pacman -Sy curl git unzip --noconfirm | |
| sudo -v ; curl https://rclone.org/install.sh | sudo bash |
| #!/usr/bin/env python | |
| # Copyright (c) 2012-2018 CORE Security Technologies | |
| # | |
| # This software is provided under under a slightly modified version | |
| # of the Apache Software License. See the accompanying LICENSE file | |
| # for more information. | |
| # | |
| # Gets logged on users via NetrWkstaUserEnum (requires admin on targets). | |
| # Mostly adapted from netview.py and lookupsid.py | |
| # |
Write a complete, production-ready, single-file HTML/JavaScript application that renders a highly detailed, photo-realistic, navigable 3D scene of the iconic cliffside village of Sidi Bou Said, Tunisia using Three.js.
- Do NOT use any external asset URLs (no external .gltf, .obj, .jpg, or .png files) as they can break or fail CORS. All textures, heights, and models must be generated dynamically and procedurally within the script (e.g., using HTML Canvas to draw textures, procedural noise algorithms for plaster and stone, or mathematical structures for 3D meshes).
- Do NOT write placeholder comments, truncated code blocks, "// TODO" markers, or "left as an exercise" shorthand. Every single function, shader, loop, and variable must be written out in its entirety.
- The output must be a single, copy-pasteable HTML file that runs perfectly immediately when opened in a browser.
- Libraries: Load Three.js and OrbitControls vi
| #!/usr/bin/env python3 | |
| # | |
| # Builds the SDL shadercross tool and installs it locally. | |
| # | |
| # Prerequisites: | |
| # Python 3 | |
| # Git | |
| # C++ compiler | |
| # CMake | |
| # Ninja |
As per here Few days ago (after an update) FF simply refused to accept my self-signed certificate anymore, Firefox requires SAN (Subject Alternative Names) present:
It must be due to removed "subject common name" fallback support from certificate validation. This fallback mode was previously enabled only for manually installed certificates. The CA Browser Forum Baseline Requirements have required the presence of the "subjectAltName" extension since 2012, and use of the subject common name was deprecated in RFC 2818. Firefox from 101.0 onward no longer use certificate CN (Common Name) for matching domain name to certificate and have migrated to only using SAN (Subject Alternate Name) so if you self sign for internal devices you’ll need to regenerate.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
